Introduction

Biking is a great and cost-effective way to get around a city, especially since more people today are cognizant of the causes behind climate change and are looking to reduce their carbon footprint. Citi Bike makes easy for New York city residents to commute to and from work everyday because of the many start and end stations provided. Thus, this visualization uses Citi Bike trip data to provide useful insights such as who bikes more between gender groups, what are the most popular start stations and more.

Inspiration

The inspiration for this visualization comes from an operating report of Citi Bike post on Medium. I appreciate how the charts and graphs included in the post were aesthetically minimal and simple to comprehend. From the data visualizations, it answered many valuable questions about NYC Citi Bike ridership for the year of 2017.

Materials Used

I used Google’s Beta Data Search tool to search for Citi Bike trip data, which was then sourced from a site called data.world. Data.world is a data catalog that allows individuals and teams to work seamlessly on the same data and share data analysis in an efficient way. The Citi Bike trip data I found only included data about ridership during the month of January 2018. Then, after retrieving the data from the site. I used Tableau to create charts and graphs to highlight different insights about NYC Citi Bike riders in January 2018. I did not restructure or clean the dataset because I thought that the dataset was already structured well.

Results

For the data visualizations, I wanted to first highlight differences in ridership between genders in order to gain more insight on the biggest Citi Bike user groups. The first visualization (Figure 1) uses a simple packed bubble graph to exemplify the total number of male vs. female who used the bike share during January 2018. I used simple colors, shades of blue and pink, to represent the two genders clearly.

Figure 2 is a line graph that displays the average number of Citi Bike starts during a given week in January. At first, it was a bit challenging to get the correct dimensions and filter because there seemed to have been an excessive number starting trips on Wednesday compared to other days. With some help, I realized that the data set included more Wednesdays than other days. So to make each day accounted for equally, I used a filter to only include dates from the 1st to the 27th of January. But overall, the line graph still showed that Wednesday is a peak day for Citi Bike for both men and women. And unsurprisingly, there are less bikers during the weekend. This insight can mean many things, but one thing I took away from this was that in NYC Citi Bike may be more likely used for commuting to and from work during the week rather than for leisure.

Dashboard of the visualization

Reflections

Overall, the most challenging part about this assignment was learning and getting familiar with Tableau. This was my first time using the tool, and I found it difficult to create different visualizations that would highlight insights in the Citi Bike dataset while also learning the tool. I would also like to create a spatial map since the dataset included longitude and latitude coordinates for both start and end stations.